【YOLOv8】YOLOv8改进系列（5）----替换主干网络之EfficientFormerV2

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 【YOLOv8改进系列】： 

【YOLOv8】YOLOv8结构解读

YOLOv8改进系列（1）----替换主干网络之EfficientViT

YOLOv8改进系列（2）----替换主干网络之FasterNet

YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2

YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck

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目录

💯一、EfficientFormerV2介绍

1. 简介

2. EfficientFormerV2 的设计

2.1 网络架构设计

2.2 网络架构示意图

2.3 关键设计选择的性能对比

3. 实验结果

3.1 ImageNet-1K 分类

3.2 下游任务

4. 关键结论

💯二、具体添加方法 

第①步：创建EfficientFormerV2.py

第②步：修改task.py 

(1)引入创建的EfficientFormerV2文件 

(2)修改_predict_once函数 

(3)修改parse_model函数

第③步：yolov8.yaml文件修改   

第④步：验证是否加入成功   

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💯一、EfficientFormerV2介绍

• 论文题目：《Rethinking Vision Transformers for MobileNet Size and Speed》
• 论文地址：https://arxiv.org/pdf/2212.08059v1

1. 简介

这篇论文介绍了一种名为 EfficientFormerV2 的新型高效视觉模型，旨在解决如何在移动设备上实现与 MobileNet 相当的模型大小和推理速度的同时，达到与 Vision Transformers (ViTs) 相似的高性能。

论文的核心目标是探索是否可以设计出一种 Transformer 模型，使其在移动设备上的推理速度和模型大小与 MobileNet 相当，同时保持高性能。为此，作者提出了 EfficientFormerV2，并通过以下方法实现这一目标：

1. 重新审视 ViTs 的设计选择，提出一种低延迟、高参数效率的改进型超网络（supernet）。

2. 引入一种细粒度的联合搜索策略，同时优化模型的延迟和参数数量，以找到高效的架构。

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2. EfficientFormerV2 的设计

2.1 网络架构设计

EfficientFormerV2 的设计基于以下关键改进：

• 统一的前馈网络（FFN）：将局部信息建模模块（如池化层）替换为深度可分离卷积（DWCONV），并将其集成到 FFN 中，简化了网络结构。

• 多头自注意力（MHSA）改进：通过在 Value 矩阵中注入局部信息，并引入 Talking Head 机制，提升注意力模块的性能。

• 高效的注意力机制：通过“Stride Attention”方法，将高分辨率特征的注意力计算简化为固定分辨率，从而减少计算复杂度。

• 注意力下采样：结合局部和全局信息的下采样策略，进一步优化性能。

2.2 网络架构示意图

EfficientFormerV2 的网络架构分为四个阶段，分别处理不同分辨率的特征（1/4、1/8、1/16 和 1/32）。前两个阶段主要使用统一的 FFN 捕获局部信息，后两个阶段结合局部 FFN 和全局 MHSA 模块，以平衡局部和全局信息的建模。

2.3 关键设计选择的性能对比

论文通过实验验证了不同设计选择对性能的影响，例如：

• 统一的 FFN 设计相比基线模型提升了 0.6% 的准确率，且没有增加延迟。

• 引入 Talking Head 和局部信息建模后，准确率进一步提升至 80.8%，同时保持参数和延迟不变。

• 通过 Stride Attention 和注意力下采样，模型在高分辨率特征上的性能和效率得到显著提升。

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3. 实验结果

3.1 ImageNet-1K 分类

EfficientFormerV2 在 ImageNet-1K 数据集上进行了广泛的实验，结果表明：

• EfficientFormerV2-S0 在与 MobileNetV2 相同的延迟和参数量下，Top-1 准确率高出 3.9%。

• EfficientFormerV2-S1 在与 MobileNetV2×1.4 相当的延迟下，准确率高出 4.3%，且模型大小减少了 2倍。

• EfficientFormerV2-L 在较大的模型规模下，达到了与 EfficientFormer-L7 相同的准确率，但模型大小减少了 3.1倍。

此外，EfficientFormerV2 在 iPhone 12 和 Pixel 6 等移动设备上的推理延迟表现出色，证明了其在实际应用中的高效性。

3.2 下游任务

EfficientFormerV2 还在目标检测、实例分割和语义分割等下游任务中进行了验证：

• 在 MS COCO 数据集上，EfficientFormerV2-L 在与 EfficientFormer-L3 相同的模型大小下，检测和分割性能分别提升了 3.3 APbox 和 2.3 APmask。

• 在 ADE20K 数据集上，EfficientFormerV2-S2 的语义分割性能（mIoU）比 PoolFormer-S12 高出 5.2%，证明了其作为特征提取器的有效性。

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4. 关键结论

EfficientFormerV2 通过重新审视 ViTs 的设计选择，并引入细粒度的联合搜索算法，成功实现了在移动设备上与 MobileNet 相当的模型大小和推理速度，同时保持了高性能。这一成果为在资源受限的硬件上部署 Transformer 模型提供了新的思路，并为未来的研究提供了有价值的参考。

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💯二、具体添加方法 

第①步：创建EfficientFormerV2.py

创建完成后，将下面代码直接复制粘贴进去：

import os
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict
import itertools
import numpy as np
from timm.models.layers import DropPath, trunc_normal_, to_2tuple__all__ = ['efficientformerv2_s0', 'efficientformerv2_s1', 'efficientformerv2_s2', 'efficientformerv2_l']EfficientFormer_width = {'L': [40, 80, 192, 384],  # 26m 83.3% 6attn'S2': [32, 64, 144, 288],  # 12m 81.6% 4attn dp0.02'S1': [32, 48, 120, 224],  # 6.1m 79.0'S0': [32, 48, 96, 176],  # 75.0 75.7
}EfficientFormer_depth = {'L': [5, 5, 15, 10],  # 26m 83.3%'S2': [4, 4, 12, 8],  # 12m'S1': [3, 3, 9, 6],  # 79.0'S0': [2, 2, 6, 4],  # 75.7
}# 26m
expansion_ratios_L = {'0': [4, 4, 4, 4, 4],'1': [4, 4, 4, 4, 4],'2': [4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],'3': [4, 4, 4, 3, 3, 3, 3, 4, 4, 4],
}# 12m
expansion_ratios_S2 = {'0': [4, 4, 4, 4],'1': [4, 4, 4, 4],'2': [4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4],'3': [4, 4, 3, 3, 3, 3, 4, 4],
}# 6.1m
expansion_ratios_S1 = {'0': [4, 4, 4],'1': [4, 4, 4],'2': [4, 4, 3, 3, 3, 3, 4, 4, 4],'3': [4, 4, 3, 3, 4, 4],
}# 3.5m
expansion_ratios_S0 = {'0': [4, 4],'1': [4, 4],'2': [4, 3, 3, 3, 4, 4],'3': [4, 3, 3, 4],
}class Attention4D(torch.nn.Module):def __init__(self, dim=384, key_dim=32, num_heads=8,attn_ratio=4,resolution=7,act_layer=nn.ReLU,stride=None):super().__init__()self.num_heads = num_headsself.scale = key_dim ** -0.5self.key_dim = key_dimself.nh_kd = nh_kd = key_dim * num_headsif stride is not None:self.resolution = math.ceil(resolution / stride)self.stride_conv = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=3, stride=stride, padding=1, groups=dim),nn.BatchNorm2d(dim), )self.upsample = nn.Upsample(scale_factor=stride, mode='bilinear')else:self.resolution = resolutionself.stride_conv = Noneself.upsample = Noneself.N = self.resolution ** 2self.N2 = self.Nself.d = int(attn_ratio * key_dim)self.dh = int(attn_ratio * key_dim) * num_headsself.attn_ratio = attn_ratioh = self.dh + nh_kd * 2self.q = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),nn.BatchNorm2d(self.num_heads * self.key_dim), )self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),nn.BatchNorm2d(self.num_heads * self.key_dim), )self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),nn.BatchNorm2d(self.num_heads * self.d),)self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,kernel_size=3, stride=1, padding=1, groups=self.num_heads * self.d),nn.BatchNorm2d(self.num_heads * self.d), )self.talking_head1 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)self.talking_head2 = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1, padding=0)self.proj = nn.Sequential(act_layer(),nn.Conv2d(self.dh, dim, 1),nn.BatchNorm2d(dim), )points = list(itertools.product(range(self.resolution), range(self.resolution)))N = len(points)attention_offsets = {}idxs = []for p1 in points:for p2 in points:offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))if offset not in attention_offsets:attention_offsets[offset] = len(attention_offsets)idxs.append(attention_offsets[offset])self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))self.register_buffer('attention_bias_idxs',torch.LongTensor(idxs).view(N, N))@torch.no_grad()def train(self, mode=True):super().train(mode)if mode and hasattr(self, 'ab'):del self.abelse:self.ab = self.attention_biases[:, self.attention_bias_idxs]def forward(self, x):  # x (B,N,C)B, C, H, W = x.shapeif self.stride_conv is not None:x = self.stride_conv(x)q = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)v = self.v(x)v_local = self.v_local(v)v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)attn = ((q @ k) * self.scale+(self.attention_biases[:, self.attention_bias_idxs]if self.training else self.ab))# attn = (q @ k) * self.scaleattn = self.talking_head1(attn)attn = attn.softmax(dim=-1)attn = self.talking_head2(attn)x = (attn @ v)out = x.transpose(2, 3).reshape(B, self.dh, self.resolution, self.resolution) + v_localif self.upsample is not None:out = self.upsample(out)out = self.proj(out)return outdef stem(in_chs, out_chs, act_layer=nn.ReLU):return nn.Sequential(nn.Conv2d(in_chs, out_chs // 2, kernel_size=3, stride=2, padding=1),nn.BatchNorm2d(out_chs // 2),act_layer(),nn.Conv2d(out_chs // 2, out_chs, kernel_size=3, stride=2, padding=1),nn.BatchNorm2d(out_chs),act_layer(),)class LGQuery(torch.nn.Module):def __init__(self, in_dim, out_dim, resolution1, resolution2):super().__init__()self.resolution1 = resolution1self.resolution2 = resolution2self.pool = nn.AvgPool2d(1, 2, 0)self.local = nn.Sequential(nn.Conv2d(in_dim, in_dim, kernel_size=3, stride=2, padding=1, groups=in_dim),)self.proj = nn.Sequential(nn.Conv2d(in_dim, out_dim, 1),nn.BatchNorm2d(out_dim), )def forward(self, x):local_q = self.local(x)pool_q = self.pool(x)q = local_q + pool_qq = self.proj(q)return qclass Attention4DDownsample(torch.nn.Module):def __init__(self, dim=384, key_dim=16, num_heads=8,attn_ratio=4,resolution=7,out_dim=None,act_layer=None,):super().__init__()self.num_heads = num_headsself.scale = key_dim ** -0.5self.key_dim = key_dimself.nh_kd = nh_kd = key_dim * num_headsself.resolution = resolutionself.d = int(attn_ratio * key_dim)self.dh = int(attn_ratio * key_dim) * num_headsself.attn_ratio = attn_ratioh = self.dh + nh_kd * 2if out_dim is not None:self.out_dim = out_dimelse:self.out_dim = dimself.resolution2 = math.ceil(self.resolution / 2)self.q = LGQuery(dim, self.num_heads * self.key_dim, self.resolution, self.resolution2)self.N = self.resolution ** 2self.N2 = self.resolution2 ** 2self.k = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.key_dim, 1),nn.BatchNorm2d(self.num_heads * self.key_dim), )self.v = nn.Sequential(nn.Conv2d(dim, self.num_heads * self.d, 1),nn.BatchNorm2d(self.num_heads * self.d),)self.v_local = nn.Sequential(nn.Conv2d(self.num_heads * self.d, self.num_heads * self.d,kernel_size=3, stride=2, padding=1, groups=self.num_heads * self.d),nn.BatchNorm2d(self.num_heads * self.d), )self.proj = nn.Sequential(act_layer(),nn.Conv2d(self.dh, self.out_dim, 1),nn.BatchNorm2d(self.out_dim), )points = list(itertools.product(range(self.resolution), range(self.resolution)))points_ = list(itertools.product(range(self.resolution2), range(self.resolution2)))N = len(points)N_ = len(points_)attention_offsets = {}idxs = []for p1 in points_:for p2 in points:size = 1offset = (abs(p1[0] * math.ceil(self.resolution / self.resolution2) - p2[0] + (size - 1) / 2),abs(p1[1] * math.ceil(self.resolution / self.resolution2) - p2[1] + (size - 1) / 2))if offset not in attention_offsets:attention_offsets[offset] = len(attention_offsets)idxs.append(attention_offsets[offset])self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))self.register_buffer('attention_bias_idxs',torch.LongTensor(idxs).view(N_, N))@torch.no_grad()def train(self, mode=True):super().train(mode)if mode and hasattr(self, 'ab'):del self.abelse:self.ab = self.attention_biases[:, self.attention_bias_idxs]def forward(self, x):  # x (B,N,C)B, C, H, W = x.shapeq = self.q(x).flatten(2).reshape(B, self.num_heads, -1, self.N2).permute(0, 1, 3, 2)k = self.k(x).flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 2, 3)v = self.v(x)v_local = self.v_local(v)v = v.flatten(2).reshape(B, self.num_heads, -1, self.N).permute(0, 1, 3, 2)attn = ((q @ k) * self.scale+(self.attention_biases[:, self.attention_bias_idxs]if self.training else self.ab))# attn = (q @ k) * self.scaleattn = attn.softmax(dim=-1)x = (attn @ v).transpose(2, 3)out = x.reshape(B, self.dh, self.resolution2, self.resolution2) + v_localout = self.proj(out)return outclass Embedding(nn.Module):def __init__(self, patch_size=3, stride=2, padding=1,in_chans=3, embed_dim=768, norm_layer=nn.BatchNorm2d,light=False, asub=False, resolution=None, act_layer=nn.ReLU, attn_block=Attention4DDownsample):super().__init__()self.light = lightself.asub = asubif self.light:self.new_proj = nn.Sequential(nn.Conv2d(in_chans, in_chans, kernel_size=3, stride=2, padding=1, groups=in_chans),nn.BatchNorm2d(in_chans),nn.Hardswish(),nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=1, padding=0),nn.BatchNorm2d(embed_dim),)self.skip = nn.Sequential(nn.Conv2d(in_chans, embed_dim, kernel_size=1, stride=2, padding=0),nn.BatchNorm2d(embed_dim))elif self.asub:self.attn = attn_block(dim=in_chans, out_dim=embed_dim,resolution=resolution, act_layer=act_layer)patch_size = to_2tuple(patch_size)stride = to_2tuple(stride)padding = to_2tuple(padding)self.conv = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,stride=stride, padding=padding)self.bn = norm_layer(embed_dim) if norm_layer else nn.Identity()else:patch_size = to_2tuple(patch_size)stride = to_2tuple(stride)padding = to_2tuple(padding)self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size,stride=stride, padding=padding)self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()def forward(self, x):if self.light:out = self.new_proj(x) + self.skip(x)elif self.asub:out_conv = self.conv(x)out_conv = self.bn(out_conv)out = self.attn(x) + out_convelse:x = self.proj(x)out = self.norm(x)return outclass Mlp(nn.Module):"""Implementation of MLP with 1*1 convolutions.Input: tensor with shape [B, C, H, W]"""def __init__(self, in_features, hidden_features=None,out_features=None, act_layer=nn.GELU, drop=0., mid_conv=False):super().__init__()out_features = out_features or in_featureshidden_features = hidden_features or in_featuresself.mid_conv = mid_convself.fc1 = nn.Conv2d(in_features, hidden_features, 1)self.act = act_layer()self.fc2 = nn.Conv2d(hidden_features, out_features, 1)self.drop = nn.Dropout(drop)self.apply(self._init_weights)if self.mid_conv:self.mid = nn.Conv2d(hidden_features, hidden_features, kernel_size=3, stride=1, padding=1,groups=hidden_features)self.mid_norm = nn.BatchNorm2d(hidden_features)self.norm1 = nn.BatchNorm2d(hidden_features)self.norm2 = nn.BatchNorm2d(out_features)def _init_weights(self, m):if isinstance(m, nn.Conv2d):trunc_normal_(m.weight, std=.02)if m.bias is not None:nn.init.constant_(m.bias, 0)def forward(self, x):x = self.fc1(x)x = self.norm1(x)x = self.act(x)if self.mid_conv:x_mid = self.mid(x)x_mid = self.mid_norm(x_mid)x = self.act(x_mid)x = self.drop(x)x = self.fc2(x)x = self.norm2(x)x = self.drop(x)return xclass AttnFFN(nn.Module):def __init__(self, dim, mlp_ratio=4.,act_layer=nn.ReLU, norm_layer=nn.LayerNorm,drop=0., drop_path=0.,use_layer_scale=True, layer_scale_init_value=1e-5,resolution=7, stride=None):super().__init__()self.token_mixer = Attention4D(dim, resolution=resolution, act_layer=act_layer, stride=stride)mlp_hidden_dim = int(dim * mlp_ratio)self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,act_layer=act_layer, drop=drop, mid_conv=True)self.drop_path = DropPath(drop_path) if drop_path > 0. \else nn.Identity()self.use_layer_scale = use_layer_scaleif use_layer_scale:self.layer_scale_1 = nn.Parameter(layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)def forward(self, x):if self.use_layer_scale:x = x + self.drop_path(self.layer_scale_1 * self.token_mixer(x))x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))else:x = x + self.drop_path(self.token_mixer(x))x = x + self.drop_path(self.mlp(x))return xclass FFN(nn.Module):def __init__(self, dim, pool_size=3, mlp_ratio=4.,act_layer=nn.GELU,drop=0., drop_path=0.,use_layer_scale=True, layer_scale_init_value=1e-5):super().__init__()mlp_hidden_dim = int(dim * mlp_ratio)self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim,act_layer=act_layer, drop=drop, mid_conv=True)self.drop_path = DropPath(drop_path) if drop_path > 0. \else nn.Identity()self.use_layer_scale = use_layer_scaleif use_layer_scale:self.layer_scale_2 = nn.Parameter(layer_scale_init_value * torch.ones(dim).unsqueeze(-1).unsqueeze(-1), requires_grad=True)def forward(self, x):if self.use_layer_scale:x = x + self.drop_path(self.layer_scale_2 * self.mlp(x))else:x = x + self.drop_path(self.mlp(x))return xdef eformer_block(dim, index, layers,pool_size=3, mlp_ratio=4.,act_layer=nn.GELU, norm_layer=nn.LayerNorm,drop_rate=.0, drop_path_rate=0.,use_layer_scale=True, layer_scale_init_value=1e-5, vit_num=1, resolution=7, e_ratios=None):blocks = []for block_idx in range(layers[index]):block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (sum(layers) - 1)mlp_ratio = e_ratios[str(index)][block_idx]if index >= 2 and block_idx > layers[index] - 1 - vit_num:if index == 2:stride = 2else:stride = Noneblocks.append(AttnFFN(dim, mlp_ratio=mlp_ratio,act_layer=act_layer, norm_layer=norm_layer,drop=drop_rate, drop_path=block_dpr,use_layer_scale=use_layer_scale,layer_scale_init_value=layer_scale_init_value,resolution=resolution,stride=stride,))else:blocks.append(FFN(dim, pool_size=pool_size, mlp_ratio=mlp_ratio,act_layer=act_layer,drop=drop_rate, drop_path=block_dpr,use_layer_scale=use_layer_scale,layer_scale_init_value=layer_scale_init_value,))blocks = nn.Sequential(*blocks)return blocksclass EfficientFormerV2(nn.Module):def __init__(self, layers, embed_dims=None,mlp_ratios=4, downsamples=None,pool_size=3,norm_layer=nn.BatchNorm2d, act_layer=nn.GELU,num_classes=1000,down_patch_size=3, down_stride=2, down_pad=1,drop_rate=0., drop_path_rate=0.,use_layer_scale=True, layer_scale_init_value=1e-5,fork_feat=True,vit_num=0,resolution=640,e_ratios=expansion_ratios_L,**kwargs):super().__init__()if not fork_feat:self.num_classes = num_classesself.fork_feat = fork_featself.patch_embed = stem(3, embed_dims[0], act_layer=act_layer)network = []for i in range(len(layers)):stage = eformer_block(embed_dims[i], i, layers,pool_size=pool_size, mlp_ratio=mlp_ratios,act_layer=act_layer, norm_layer=norm_layer,drop_rate=drop_rate,drop_path_rate=drop_path_rate,use_layer_scale=use_layer_scale,layer_scale_init_value=layer_scale_init_value,resolution=math.ceil(resolution / (2 ** (i + 2))),vit_num=vit_num,e_ratios=e_ratios)network.append(stage)if i >= len(layers) - 1:breakif downsamples[i] or embed_dims[i] != embed_dims[i + 1]:# downsampling between two stagesif i >= 2:asub = Trueelse:asub = Falsenetwork.append(Embedding(patch_size=down_patch_size, stride=down_stride,padding=down_pad,in_chans=embed_dims[i], embed_dim=embed_dims[i + 1],resolution=math.ceil(resolution / (2 ** (i + 2))),asub=asub,act_layer=act_layer, norm_layer=norm_layer,))self.network = nn.ModuleList(network)if self.fork_feat:# add a norm layer for each outputself.out_indices = [0, 2, 4, 6]for i_emb, i_layer in enumerate(self.out_indices):if i_emb == 0 and os.environ.get('FORK_LAST3', None):layer = nn.Identity()else:layer = norm_layer(embed_dims[i_emb])layer_name = f'norm{i_layer}'self.add_module(layer_name, layer)self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, resolution, resolution))]def forward_tokens(self, x):outs = []for idx, block in enumerate(self.network):x = block(x)if self.fork_feat and idx in self.out_indices:norm_layer = getattr(self, f'norm{idx}')x_out = norm_layer(x)outs.append(x_out)return outsdef forward(self, x):x = self.patch_embed(x)x = self.forward_tokens(x)return xdef update_weight(model_dict, weight_dict):idx, temp_dict = 0, {}for k, v in weight_dict.items():if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):temp_dict[k] = vidx += 1model_dict.update(temp_dict)print(f'loading weights... {idx}/{len(model_dict)} items')return model_dictdef efficientformerv2_s0(weights='', **kwargs):model = EfficientFormerV2(layers=EfficientFormer_depth['S0'],embed_dims=EfficientFormer_width['S0'],downsamples=[True, True, True, True, True],vit_num=2,drop_path_rate=0.0,e_ratios=expansion_ratios_S0,**kwargs)if weights:pretrained_weight = torch.load(weights)['model']model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef efficientformerv2_s1(weights='', **kwargs):model = EfficientFormerV2(layers=EfficientFormer_depth['S1'],embed_dims=EfficientFormer_width['S1'],downsamples=[True, True, True, True],vit_num=2,drop_path_rate=0.0,e_ratios=expansion_ratios_S1,**kwargs)if weights:pretrained_weight = torch.load(weights)['model']model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef efficientformerv2_s2(weights='', **kwargs):model = EfficientFormerV2(layers=EfficientFormer_depth['S2'],embed_dims=EfficientFormer_width['S2'],downsamples=[True, True, True, True],vit_num=4,drop_path_rate=0.02,e_ratios=expansion_ratios_S2,**kwargs)if weights:pretrained_weight = torch.load(weights)['model']model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modeldef efficientformerv2_l(weights='', **kwargs):model = EfficientFormerV2(layers=EfficientFormer_depth['L'],embed_dims=EfficientFormer_width['L'],downsamples=[True, True, True, True],vit_num=6,drop_path_rate=0.1,e_ratios=expansion_ratios_L,**kwargs)if weights:pretrained_weight = torch.load(weights)['model']model.load_state_dict(update_weight(model.state_dict(), pretrained_weight))return modelif __name__ == '__main__':inputs = torch.randn((1, 3, 640, 640))model = efficientformerv2_s0('eformer_s0_450.pth')res = model(inputs)for i in res:print(i.size())model = efficientformerv2_s1('eformer_s1_450.pth')res = model(inputs)for i in res:print(i.size())model = efficientformerv2_s2('eformer_s2_450.pth')res = model(inputs)for i in res:print(i.size())model = efficientformerv2_l('eformer_l_450.pth')res = model(inputs)for i in res:print(i.size())

第②步：修改task.py 

(1)引入创建的EfficientFormerV2文件 

​

from ultralytics.nn.backbone.EfficientFormerV2 import *

(2)修改_predict_once函数 

def _predict_once(self, x, profile=False, visualize=False, embed=None):"""Perform a forward pass through the network.Args:x (torch.Tensor): The input tensor to the model.profile (bool):  Print the computation time of each layer if True, defaults to False.visualize (bool): Save the feature maps of the model if True, defaults to False.embed (list, optional): A list of feature vectors/embeddings to return.Returns:(torch.Tensor): The last output of the model."""y, dt, embeddings = [], [], []  # outputsfor idx, m in enumerate(self.model):if m.f != -1:  # if not from previous layerx = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layersif profile:self._profile_one_layer(m, x, dt)if hasattr(m, 'backbone'):x = m(x)for _ in range(5 - len(x)):x.insert(0, None)for i_idx, i in enumerate(x):if i_idx in self.save:y.append(i)else:y.append(None)# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')x = x[-1]else:x = m(x)  # runy.append(x if m.i in self.save else None)  # save output# if type(x) in {list, tuple}:#     if idx == (len(self.model) - 1):#         if type(x[1]) is dict:#             print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]["one2one"]])}')#         else:#             print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]])}')#     else:#         print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')# elif type(x) is dict:#     print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x["one2one"]])}')# else:#     if not hasattr(m, 'backbone'):#         print(f'layer id:{idx:>2} {m.type:>50} output shape:{x.size()}')if visualize:feature_visualization(x, m.type, m.i, save_dir=visualize)if embed and m.i in embed:embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1))  # flattenif m.i == max(embed):return torch.unbind(torch.cat(embeddings, 1), dim=0)return x

(3)修改parse_model函数

可以直接把下面的代码粘贴到对应的位置中，后续的改进中，对应的模块就不需要做出改变，有改变处，后续会另有说明

def parse_model(d, ch, verbose=True, warehouse_manager=None):  # model_dict, input_channels(3)"""Parse a YOLO model.yaml dictionary into a PyTorch model."""import ast# Argsmax_channels = float("inf")nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))if scales:scale = d.get("scale")if not scale:scale = tuple(scales.keys())[0]LOGGER.warning(f"WARNING ⚠️ no model scale passed. Assuming scale='{scale}'.")if len(scales[scale]) == 3:depth, width, max_channels = scales[scale]elif len(scales[scale]) == 4:depth, width, max_channels, threshold = scales[scale]if act:Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()if verbose:LOGGER.info(f"{colorstr('activation:')} {act}")  # printif verbose:LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<60}{'arguments':<50}")ch = [ch]layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch outis_backbone = Falsefor i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]):  # from, number, module, argstry:if m == 'node_mode':m = d[m]if len(args) > 0:if args[0] == 'head_channel':args[0] = int(d[args[0]])t = mm = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m]  # get moduleexcept:passfor j, a in enumerate(args):if isinstance(a, str):with contextlib.suppress(ValueError):try:args[j] = locals()[a] if a in locals() else ast.literal_eval(a)except:args[j] = an = n_ = max(round(n * depth), 1) if n > 1 else n  # depth gainif m in {Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, BottleneckCSP, C1, C2, C2f, ELAN1, AConv, SPPELAN, C2fAttn, C3, C3TR, C3Ghost, nn.Conv2d, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3, PSA, SCDown, C2fCIB, C2f_Faster, C2f_ODConv,C2f_Faster_EMA, C2f_DBB, GSConv, GSConvns, VoVGSCSP, VoVGSCSPns, VoVGSCSPC, C2f_CloAtt, C3_CloAtt, SCConv, C2f_SCConv, C3_SCConv, C2f_ScConv, C3_ScConv,C3_EMSC, C3_EMSCP, C2f_EMSC, C2f_EMSCP, RCSOSA, KWConv, C2f_KW, C3_KW, DySnakeConv, C2f_DySnakeConv, C3_DySnakeConv,DCNv2, C3_DCNv2, C2f_DCNv2, DCNV3_YOLO, C3_DCNv3, C2f_DCNv3, C3_Faster, C3_Faster_EMA, C3_ODConv,OREPA, OREPA_LargeConv, RepVGGBlock_OREPA, C3_OREPA, C2f_OREPA, C3_DBB, C3_REPVGGOREPA, C2f_REPVGGOREPA,C3_DCNv2_Dynamic, C2f_DCNv2_Dynamic, C3_ContextGuided, C2f_ContextGuided, C3_MSBlock, C2f_MSBlock,C3_DLKA, C2f_DLKA, CSPStage, SPDConv, RepBlock, C3_EMBC, C2f_EMBC, SPPF_LSKA, C3_DAttention, C2f_DAttention,C3_Parc, C2f_Parc, C3_DWR, C2f_DWR, RFAConv, RFCAConv, RFCBAMConv, C3_RFAConv, C2f_RFAConv,C3_RFCBAMConv, C2f_RFCBAMConv, C3_RFCAConv, C2f_RFCAConv, C3_FocusedLinearAttention, C2f_FocusedLinearAttention,C3_AKConv, C2f_AKConv, AKConv, C3_MLCA, C2f_MLCA,C3_UniRepLKNetBlock, C2f_UniRepLKNetBlock, C3_DRB, C2f_DRB, C3_DWR_DRB, C2f_DWR_DRB, CSP_EDLAN,C3_AggregatedAtt, C2f_AggregatedAtt, DCNV4_YOLO, C3_DCNv4, C2f_DCNv4, HWD, SEAM,C3_SWC, C2f_SWC, C3_iRMB, C2f_iRMB, C3_iRMB_Cascaded, C2f_iRMB_Cascaded, C3_iRMB_DRB, C2f_iRMB_DRB, C3_iRMB_SWC, C2f_iRMB_SWC,C3_VSS, C2f_VSS, C3_LVMB, C2f_LVMB, RepNCSPELAN4, DBBNCSPELAN4, OREPANCSPELAN4, DRBNCSPELAN4, ADown, V7DownSampling,C3_DynamicConv, C2f_DynamicConv, C3_GhostDynamicConv, C2f_GhostDynamicConv, C3_RVB, C2f_RVB, C3_RVB_SE, C2f_RVB_SE, C3_RVB_EMA, C2f_RVB_EMA, DGCST,C3_RetBlock, C2f_RetBlock, C3_PKIModule, C2f_PKIModule, RepNCSPELAN4_CAA, C3_FADC, C2f_FADC, C3_PPA, C2f_PPA, SRFD, DRFD, RGCSPELAN,C3_Faster_CGLU, C2f_Faster_CGLU, C3_Star, C2f_Star, C3_Star_CAA, C2f_Star_CAA, C3_KAN, C2f_KAN, C3_EIEM, C2f_EIEM, C3_DEConv, C2f_DEConv,C3_SMPCGLU, C2f_SMPCGLU, C3_Heat, C2f_Heat, CSP_PTB, SimpleStem, VisionClueMerge, VSSBlock_YOLO, XSSBlock, GLSA, C2f_WTConv, WTConv2d, FeaturePyramidSharedConv,C2f_FMB, LDConv, C2f_gConv, C2f_WDBB, C2f_DeepDBB, C2f_AdditiveBlock, C2f_AdditiveBlock_CGLU, CSP_MSCB, C2f_MSMHSA_CGLU, CSP_PMSFA, C2f_MogaBlock,C2f_SHSA, C2f_SHSA_CGLU, C2f_SMAFB, C2f_SMAFB_CGLU, C2f_IdentityFormer, C2f_RandomMixing, C2f_PoolingFormer, C2f_ConvFormer, C2f_CaFormer,C2f_IdentityFormerCGLU, C2f_RandomMixingCGLU, C2f_PoolingFormerCGLU, C2f_ConvFormerCGLU, C2f_CaFormerCGLU, CSP_MutilScaleEdgeInformationEnhance, C2f_FFCM,C2f_SFHF, CSP_FreqSpatial, C2f_MSM, C2f_RAB, C2f_HDRAB, C2f_LFE, CSP_MutilScaleEdgeInformationSelect, C2f_SFA, C2f_CTA, C2f_CAMixer, MANet,MANet_FasterBlock, MANet_FasterCGLU, MANet_Star, C2f_HFERB, C2f_DTAB, C2f_ETB, C2f_JDPM, C2f_AP, PSConv, C2f_Kat, C2f_Faster_KAN, C2f_Strip, C2f_StripCGLU}:if args[0] == 'head_channel':args[0] = d[args[0]]c1, c2 = ch[f], args[0]if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)c2 = make_divisible(min(c2, max_channels) * width, 8)if m is C2fAttn:args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8)  # embed channelsargs[2] = int(max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2])  # num headsargs = [c1, c2, *args[1:]]if m in (KWConv, C2f_KW, C3_KW):args.insert(2, f'layer{i}')args.insert(2, warehouse_manager)if m in (DySnakeConv,):c2 = c2 * 3if m in (RepNCSPELAN4, DBBNCSPELAN4, OREPANCSPELAN4, DRBNCSPELAN4, RepNCSPELAN4_CAA):args[2] = make_divisible(min(args[2], max_channels) * width, 8)args[3] = make_divisible(min(args[3], max_channels) * width, 8)if m in {BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3, C2fCIB, C2f_Faster, C2f_ODConv, C2f_Faster_EMA, C2f_DBB,VoVGSCSP, VoVGSCSPns, VoVGSCSPC, C2f_CloAtt, C3_CloAtt, C2f_SCConv, C3_SCConv, C2f_ScConv, C3_ScConv,C3_EMSC, C3_EMSCP, C2f_EMSC, C2f_EMSCP, RCSOSA, C2f_KW, C3_KW, C2f_DySnakeConv, C3_DySnakeConv,C3_DCNv2, C2f_DCNv2, C3_DCNv3, C2f_DCNv3, C3_Faster, C3_Faster_EMA, C3_ODConv, C3_OREPA, C2f_OREPA, C3_DBB,C3_REPVGGOREPA, C2f_REPVGGOREPA, C3_DCNv2_Dynamic, C2f_DCNv2_Dynamic, C3_ContextGuided, C2f_ContextGuided, C3_MSBlock, C2f_MSBlock, C3_DLKA, C2f_DLKA, CSPStage, RepBlock, C3_EMBC, C2f_EMBC, C3_DAttention, C2f_DAttention,C3_Parc, C2f_Parc, C3_DWR, C2f_DWR, C3_RFAConv, C2f_RFAConv, C3_RFCBAMConv, C2f_RFCBAMConv, C3_RFCAConv, C2f_RFCAConv,C3_FocusedLinearAttention, C2f_FocusedLinearAttention, C3_AKConv, C2f_AKConv, C3_MLCA, C2f_MLCA,C3_UniRepLKNetBlock, C2f_UniRepLKNetBlock, C3_DRB, C2f_DRB, C3_DWR_DRB, C2f_DWR_DRB, CSP_EDLAN,C3_AggregatedAtt, C2f_AggregatedAtt, C3_DCNv4, C2f_DCNv4, C3_SWC, C2f_SWC,C3_iRMB, C2f_iRMB, C3_iRMB_Cascaded, C2f_iRMB_Cascaded, C3_iRMB_DRB, C2f_iRMB_DRB, C3_iRMB_SWC, C2f_iRMB_SWC,C3_VSS, C2f_VSS, C3_LVMB, C2f_LVMB, C3_DynamicConv, C2f_DynamicConv, C3_GhostDynamicConv, C2f_GhostDynamicConv,C3_RVB, C2f_RVB, C3_RVB_SE, C2f_RVB_SE, C3_RVB_EMA, C2f_RVB_EMA, C3_RetBlock, C2f_RetBlock, C3_PKIModule, C2f_PKIModule,C3_FADC, C2f_FADC, C3_PPA, C2f_PPA, RGCSPELAN, C3_Faster_CGLU, C2f_Faster_CGLU, C3_Star, C2f_Star, C3_Star_CAA, C2f_Star_CAA,C3_KAN, C2f_KAN, C3_EIEM, C2f_EIEM, C3_DEConv, C2f_DEConv, C3_SMPCGLU, C2f_SMPCGLU, C3_Heat, C2f_Heat, CSP_PTB, XSSBlock, C2f_WTConv,C2f_FMB, C2f_gConv, C2f_WDBB, C2f_DeepDBB, C2f_AdditiveBlock, C2f_AdditiveBlock_CGLU, CSP_MSCB, C2f_MSMHSA_CGLU, CSP_PMSFA, C2f_MogaBlock,C2f_SHSA, C2f_SHSA_CGLU, C2f_SMAFB, C2f_SMAFB_CGLU, C2f_IdentityFormer, C2f_RandomMixing, C2f_PoolingFormer, C2f_ConvFormer, C2f_CaFormer,C2f_IdentityFormerCGLU, C2f_RandomMixingCGLU, C2f_PoolingFormerCGLU, C2f_ConvFormerCGLU, C2f_CaFormerCGLU, CSP_MutilScaleEdgeInformationEnhance, C2f_FFCM,C2f_SFHF, CSP_FreqSpatial, C2f_MSM, C2f_RAB, C2f_HDRAB, C2f_LFE, CSP_MutilScaleEdgeInformationSelect, C2f_SFA, C2f_CTA, C2f_CAMixer, MANet,MANet_FasterBlock, MANet_FasterCGLU, MANet_Star, C2f_HFERB, C2f_DTAB, C2f_ETB, C2f_JDPM, C2f_AP, C2f_Kat, C2f_Faster_KAN, C2f_Strip, C2f_StripCGLU}:args.insert(2, n)  # number of repeatsn = 1elif m in {AIFI, AIFI_RepBN}:args = [ch[f], *args]c2 = args[0]elif m in (HGStem, HGBlock, Ghost_HGBlock, Rep_HGBlock, Dynamic_HGBlock, EIEStem):c1, cm, c2 = ch[f], args[0], args[1]if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)c2 = make_divisible(min(c2, max_channels) * width, 8)cm = make_divisible(min(cm, max_channels) * width, 8)args = [c1, cm, c2, *args[2:]]if m in (HGBlock, Ghost_HGBlock, Rep_HGBlock, Dynamic_HGBlock):args.insert(4, n)  # number of repeatsn = 1elif m is ResNetLayer:c2 = args[1] if args[3] else args[1] * 4elif m is nn.BatchNorm2d:args = [ch[f]]elif m is Concat:c2 = sum(ch[x] for x in f)elif m in ((WorldDetect, ImagePoolingAttn) + DETECT_CLASS + V10_DETECT_CLASS + SEGMENT_CLASS + POSE_CLASS + OBB_CLASS):args.append([ch[x] for x in f])if m in SEGMENT_CLASS:args[2] = make_divisible(min(args[2], max_channels) * width, 8)if m in (Segment_LSCD, Segment_TADDH, Segment_LSCSBD, Segment_LSDECD, Segment_RSCD):args[3] = make_divisible(min(args[3], max_channels) * width, 8)if m in (Detect_LSCD, Detect_TADDH, Detect_LSCSBD, Detect_LSDECD, Detect_RSCD, v10Detect_LSCD, v10Detect_TADDH, v10Detect_RSCD, v10Detect_LSDECD):args[1] = make_divisible(min(args[1], max_channels) * width, 8)if m in (Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD):args[2] = make_divisible(min(args[2], max_channels) * width, 8)elif m is RTDETRDecoder:  # special case, channels arg must be passed in index 1args.insert(1, [ch[x] for x in f])elif m is Fusion:args[0] = d[args[0]]c1, c2 = [ch[x] for x in f], (sum([ch[x] for x in f]) if args[0] == 'concat' else ch[f[0]])args = [c1, args[0]]elif m is CBLinear:c2 = make_divisible(min(args[0][-1], max_channels) * width, 8)c1 = ch[f]args = [c1, [make_divisible(min(c2_, max_channels) * width, 8) for c2_ in args[0]], *args[1:]]elif m is CBFuse:c2 = ch[f[-1]]elif isinstance(m, str):t = mif len(args) == 2:        m = timm.create_model(m, pretrained=args[0], pretrained_cfg_overlay={'file':args[1]}, features_only=True)elif len(args) == 1:m = timm.create_model(m, pretrained=args[0], features_only=True)c2 = m.feature_info.channels()elif m in {convnextv2_atto, convnextv2_femto, convnextv2_pico, convnextv2_nano, convnextv2_tiny, convnextv2_base, convnextv2_large, convnextv2_huge,fasternet_t0, fasternet_t1, fasternet_t2, fasternet_s, fasternet_m, fasternet_l,EfficientViT_M0, EfficientViT_M1, EfficientViT_M2, EfficientViT_M3, EfficientViT_M4, EfficientViT_M5,efficientformerv2_s0, efficientformerv2_s1, efficientformerv2_s2, efficientformerv2_l,vanillanet_5, vanillanet_6, vanillanet_7, vanillanet_8, vanillanet_9, vanillanet_10, vanillanet_11, vanillanet_12, vanillanet_13, vanillanet_13_x1_5, vanillanet_13_x1_5_ada_pool,RevCol,lsknet_t, lsknet_s,SwinTransformer_Tiny,repvit_m0_9, repvit_m1_0, repvit_m1_1, repvit_m1_5, repvit_m2_3,CSWin_tiny, CSWin_small, CSWin_base, CSWin_large,unireplknet_a, unireplknet_f, unireplknet_p, unireplknet_n, unireplknet_t, unireplknet_s, unireplknet_b, unireplknet_l, unireplknet_xl,transnext_micro, transnext_tiny, transnext_small, transnext_base,RMT_T, RMT_S, RMT_B, RMT_L,PKINET_T, PKINET_S, PKINET_B,MobileNetV4ConvSmall, MobileNetV4ConvMedium, MobileNetV4ConvLarge, MobileNetV4HybridMedium, MobileNetV4HybridLarge,starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4}:if m is RevCol:args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]]args[2] = [max(round(k * depth), 1) for k in args[2]]m = m(*args)c2 = m.channelelif m in {EMA, SpatialAttention, BiLevelRoutingAttention, BiLevelRoutingAttention_nchw,TripletAttention, CoordAtt, CBAM, BAMBlock, LSKBlock, ScConv, LAWDS, EMSConv, EMSConvP,SEAttention, CPCA, Partial_conv3, FocalModulation, EfficientAttention, MPCA, deformable_LKA,EffectiveSEModule, LSKA, SegNext_Attention, DAttention, MLCA, TransNeXt_AggregatedAttention,FocusedLinearAttention, LocalWindowAttention, ChannelAttention_HSFPN, ELA_HSFPN, CA_HSFPN, CAA_HSFPN, DySample, CARAFE, CAA, ELA, CAFM, AFGCAttention, EUCB, ContrastDrivenFeatureAggregation, FSA}:c2 = ch[f]args = [c2, *args]# print(args)elif m in {SimAM, SpatialGroupEnhance}:c2 = ch[f]elif m is ContextGuidedBlock_Down:c2 = ch[f] * 2args = [ch[f], c2, *args]elif m is BiFusion:c1 = [ch[x] for x in f]c2 = make_divisible(min(args[0], max_channels) * width, 8)args = [c1, c2]# --------------GOLD-YOLO--------------elif m in {SimFusion_4in, AdvPoolFusion}:c2 = sum(ch[x] for x in f)elif m is SimFusion_3in:c2 = args[0]if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)c2 = make_divisible(min(c2, max_channels) * width, 8)args = [[ch[f_] for f_ in f], c2]elif m is IFM:c1 = ch[f]c2 = sum(args[0])args = [c1, *args]elif m is InjectionMultiSum_Auto_pool:c1 = ch[f[0]]c2 = args[0]args = [c1, *args]elif m is PyramidPoolAgg:c2 = args[0]args = [sum([ch[f_] for f_ in f]), *args]elif m is TopBasicLayer:c2 = sum(args[1])# --------------GOLD-YOLO--------------# --------------ASF--------------elif m is Zoom_cat:c2 = sum(ch[x] for x in f)elif m is Add:c2 = ch[f[-1]]elif m in {ScalSeq, DynamicScalSeq}:c1 = [ch[x] for x in f]c2 = make_divisible(args[0] * width, 8)args = [c1, c2]elif m is asf_attention_model:args = [ch[f[-1]]]# --------------ASF--------------elif m is SDI:args = [[ch[x] for x in f]]elif m is Multiply:c2 = ch[f[0]]elif m is FocusFeature:c1 = [ch[x] for x in f]c2 = int(c1[1] * 0.5 * 3)args = [c1, *args]elif m is DASI:c1 = [ch[x] for x in f]args = [c1, c2]elif m is CSMHSA:c1 = [ch[x] for x in f]c2 = ch[f[-1]]args = [c1, c2]elif m is CFC_CRB:c1 = ch[f]c2 = c1 // 2args = [c1, *args]elif m is SFC_G2:c1 = [ch[x] for x in f]c2 = c1[0]args = [c1]elif m in {CGAFusion, CAFMFusion, SDFM, PSFM}:c2 = ch[f[1]]args = [c2, *args]elif m in {ContextGuideFusionModule}:c1 = [ch[x] for x in f]c2 = 2 * c1[1]args = [c1]# elif m in {PSA}:#     c2 = ch[f]#     args = [c2, *args]elif m in {SBA}:c1 = [ch[x] for x in f]c2 = c1[-1]args = [c1, c2]elif m in {WaveletPool}:c2 = ch[f] * 4elif m in {WaveletUnPool}:c2 = ch[f] // 4elif m in {CSPOmniKernel}:c2 = ch[f]args = [c2]elif m in {ChannelTransformer, PyramidContextExtraction}:c1 = [ch[x] for x in f]c2 = c1args = [c1]elif m in {RCM}:c2 = ch[f]args = [c2, *args]elif m in {DynamicInterpolationFusion}:c2 = ch[f[0]]args = [[ch[x] for x in f]]elif m in {FuseBlockMulti}:c2 = ch[f[0]]args = [c2]elif m in {CrossLayerChannelAttention, CrossLayerSpatialAttention}:c2 = [ch[x] for x in f]args = [c2[0], *args]elif m in {FreqFusion}:c2 = ch[f[0]]args = [[ch[x] for x in f], *args]elif m in {DynamicAlignFusion}:c2 = args[0]args = [[ch[x] for x in f], c2]elif m in {ConvEdgeFusion}:c2 = make_divisible(min(args[0], max_channels) * width, 8)args = [[ch[x] for x in f], c2]elif m in {MutilScaleEdgeInfoGenetator}:c1 = ch[f]c2 = [make_divisible(min(i, max_channels) * width, 8) for i in args[0]]args = [c1, c2]elif m in {MultiScaleGatedAttn}:c1 = [ch[x] for x in f]c2 = min(c1)args = [c1]elif m in {WFU, MultiScalePCA, MultiScalePCA_Down}:c1 = [ch[x] for x in f]c2 = c1[0]args = [c1]elif m in {GetIndexOutput}:c2 = ch[f][args[0]]elif m is HyperComputeModule:c1, c2 = ch[f], args[0]c2 = make_divisible(min(c2, max_channels) * width, 8)args = [c1, c2, threshold]else:c2 = ch[f]if isinstance(c2, list) and m not in {ChannelTransformer, PyramidContextExtraction, CrossLayerChannelAttention, CrossLayerSpatialAttention, MutilScaleEdgeInfoGenetator}:is_backbone = Truem_ = mm_.backbone = Trueelse:m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # modulet = str(m)[8:-2].replace('__main__.', '')  # module typem.np = sum(x.numel() for x in m_.parameters())  # number paramsm_.i, m_.f, m_.type = i + 4 if is_backbone else i, f, t  # attach index, 'from' index, typeif verbose:LOGGER.info(f"{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<60}{str(args):<50}")  # printsave.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelistlayers.append(m_)if i == 0:ch = []if isinstance(c2, list) and m not in {ChannelTransformer, PyramidContextExtraction, CrossLayerChannelAttention, CrossLayerSpatialAttention, MutilScaleEdgeInfoGenetator}:ch.extend(c2)for _ in range(5 - len(ch)):ch.insert(0, 0)else:ch.append(c2)return nn.Sequential(*layers), sorted(save)

第③步：yolov8.yaml文件修改   

在下述文件夹中创立yolov8-EfficientFormerV2.yaml

​

# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, efficientformerv2_s0, []]  # 4- [-1, 1, SPPF, [1024, 5]]  # 5# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6- [[-1, 3], 1, Concat, [1]]  # 7 cat backbone P4- [-1, 3, C2f, [512]]  # 8- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9- [[-1, 2], 1, Concat, [1]]  # 10 cat backbone P3- [-1, 3, C2f, [256]]  # 11 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]] # 12- [[-1, 8], 1, Concat, [1]]  # 13 cat head P4- [-1, 3, C2f, [512]]  # 14 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]] # 15- [[-1, 5], 1, Concat, [1]]  # 16 cat head P5- [-1, 3, C2f, [1024]]  # 17 (P5/32-large)- [[11, 14, 17], 1, Detect, [nc]]  # Detect(P3, P4, P5)

第④步：验证是否加入成功   

将train.py中的配置文件进行修改，并运行  

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🏋不是每一粒种子都能开花，但播下种子就比荒芜的旷野强百倍🏋

🍁YOLOv8入门+改进专栏🍁

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 【YOLOv8改进系列】： 

【YOLOv8】YOLOv8结构解读

YOLOv8改进系列（1）----替换主干网络之EfficientViT

YOLOv8改进系列（2）----替换主干网络之FasterNet

YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2

YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck

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